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Record W2775088297 · doi:10.1097/pep.0000000000000469

Interrelationships of Functional Status and Health Conditions in Children With Cerebral Palsy: A Descriptive Study

2017· article· en· W2775088297 on OpenAlexafffundabout
Doreen J. Bartlett, Emily Dyszuk, Barbara Galuppi, Jan Willem Gorter

Bibliographic record

VenuePediatric Physical Therapy · 2017
Typearticle
Languageen
FieldMedicine
TopicCerebral Palsy and Movement Disorders
Canadian institutionsMcMaster UniversityUniversity of SudburyWestern University
FundersCanadian Institutes of Health Research
KeywordsGross Motor Function Classification SystemCerebral palsyPhysical medicine and rehabilitationMotor functionMedicinePsychologyPhysical therapy

Abstract

fetched live from OpenAlex

PURPOSE: To examine the relationship among the Gross Motor Function Classification System (GMFCS), the Manual Ability Classification System (MACS), and the Communication Function Classification System (CFCS) in children with cerebral palsy (CP) and to determine the average number and effect of health conditions. METHODS: Participants were 671 children with CP aged 2 to 12 years from Canada and the United States. Cross-tabulation of functional classifications and averages were computed for the number and impact of health conditions and comparisons among groups. RESULTS: A total of 78 of the 125 possible classification combinations were recorded. Most frequent were GMFCS I, MACS I, CFCS I; GMFCS I, MACS II, CFCS I; and GMFCS II, MACS II, CFCS I. With lower levels of function, the average number and average impact of associated health conditions increased. CONCLUSIONS: The use of functional profiles across classification systems, with data on the associated health conditions, provides a more comprehensive picture of CP than any single classification or measure.

Fetched live from OpenAlex and de-inverted. Abstracts are not stored in this database: the inverted indexes are 8.6 GB of the frame’s 9.3 GB of text, and the host has 13 GB free.

How this classification was reachedexpand

Full frame distilled prediction

Teacher imitation

Not calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.

metaresearch head score (Codex)0.000
metaresearch head score (Gemma)0.000
Version: codex-gemma-dda1882f352aValidation status: machine_predicted_unvalidated
Candidate categoriesnone
Consensus categoriesnone
DomainCandidate signal: none · Consensus signal: none
Study designCandidate signal: Observational · Consensus signal: Observational
GenreCandidate signal: Empirical · Consensus signal: Empirical
Teacher disagreement score0.006
Threshold uncertainty score0.339

Codex and Gemma teacher scores by category

CategoryCodexGemma
Metaresearch0.0000.000
Meta-epidemiology (narrow)0.0000.000
Meta-epidemiology (broad)0.0000.000
Bibliometrics0.0000.000
Science and technology studies0.0000.000
Scholarly communication0.0000.000
Open science0.0000.000
Research integrity0.0000.000
Insufficient payload (model declined to judge)0.0000.000

Machine scores (provisional)

The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.

Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.

Opus teacher head0.055
GPT teacher head0.328
Teacher spread0.273 · how far apart the two teachers sit on this one work
Validation statusscore_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from it

Classification

machine, unvalidated

Machine predicted; a candidate call from one teacher head, not a consensus.

The models applied no category: nothing in the taxonomy fit this work.
Study designObservational
Domainnot available
GenreEmpirical

How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".

Quick stats

Citations9
Published2017
Admission routes3
Has abstractyes

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